Considering remaining capacity of our desktop, we only utilize S3DIS Dataset.

bash prepare_data.sh # from author's code

We use all modules, config files, dataset preprocessing, train, test, evaluation, bottom-up and top-down refinement etc codes from author's codes. We use pretrained model from HAIS checkpoint We only redefine some of the details in MLP and Loss function. The number of MLP layers is defined as 2 but we figured out that it is quite shallow to learn point-wise semantic scores and offsets. Also we put more weights on semantic loss and offset loss which we believe has more significant impact on performance.

THESE ARE THE ONLY MODIFICATIONS THAT WE PERFORM. OTHER THAN THIS, EVERYTHING IS FROM AUTHOR'S CODE

1. The number of MLP layers : 2 -> 3 (we thought 2 is not enough to learn per-point features such as semantic scores and offset coordinates)
2. Activation function for MLP : ReLU -> LeakyReLU (To prevent side effect stemmed from the increased layers)
3. Loss function : multi-task loss -> weighted multi-task loss (details on the report)
4. Epochs : 20 -> 8 (to reduce training time)

pip install spconv-cu102 # we used cuda 11.3 version
pip install -r requirements.txt
sudo apt-get install libsparsehash-dev
python setup.py build_ext develop

python train.py --config configs/softgroup_s3dis_backbone_fold5.yaml --work_dir $WORK_DIR --skip_validate # from author's code
python train.py --config configs/softgroup_s3dis_fold5.yaml --work_dir $WORK_DIR --skip_validate # from author's code

python test.py --config configs/softgroup_s3dis_fold5.yaml --out $RESULT --checkpoint $CHECKPOINT # from author's code